The present invention relates generally to optimization methods and machine learning and in particular to implementation of Multiple Kernel Learning (“MKL”) methods in support vector machines (“SVM”).
Multiple Kernel Learning (“MKL”) methods are used to solve classification and regression problems involving multimodal data and machine learning. In machine learning, support vector machines (SVM) are applied to analyze data and recognize patterns, used for classification and regression analysis. More specifically, the application of MKL methods in SVM's can be used to solve various real world problems, such as classification of images, classification of proteins, recognizing hand-written characters, and biometric identity recognition. Generally, MKL methods are applied in situations where the available data involves multiple, heterogeneous data sources. In this case, each kernel may represent the similarity between data points in different modalities. In many cases, a successful identification requires that the object will be similar in both (or all) feature representations. Therefore, a sum of products of kernels is ideal. However, finding the optimal parameters for the sum of products of kernels is a high dimensional optimization problem, as the number of parameters is quadratic in the number of kernels. As a result, the increase in the number of parameters may result in the risk of overfitting data.